Solving the Traveling Salesman Problem through Genetic Algorithms with Changing Crossover Operators
Ryouei Takahashi
Abstract
Ryouei Takahashi
Abstract
In order to solve the traveling salesman problem (TSP) through genetic algorithms (GAs), a method of changing crossover operators (CXO), which can flexibly substitute the current crossover operator for another suitable crossover operator at any time, is proposed. This paper reports experimental validation of CXO through C software by using data of 200 cities.
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In order to solve the traveling salesman problem (TSP) through genetic algorithms (GAs), a method of changing crossover operators (CXO), which can flexibly substitute the current crossover operator for another suitable crossover operator at any time, is proposed. This paper reports experimental validation of CXO through C software by using data of 200 cities.
Key concepts: Crossover, Travelling salesman problem, Operator (biology), Genetic algorithm, Computer science, Mathematical optimization, Bottleneck traveling salesman problem, 2-opt